As machine learning ubiquitously enhances what can be done with computers and devices, the influx of network adjacent models will continue to increase. Many of these models will essentially be a reincarnation of robustly pre-trained public models or models trained on very similar data, solving highly comparable tasks. Optimally, collaborative learning could enhance and debiasing all equivalent models to better benefit all such machine learning tasks [1]}, [2]}. However, in reality, many models will require private data to become highly localized finely tuned solutions, and disinclination towards sharing private data will prevent this idealistic approach. Additionally, while federated solutions allow for some consolidation of models, federation will retract the benefit of private local models or personalized individual models.
